What is AI code review and what are the benefits?
Artificial intelligence (AI) is rapidly advancing, and one of the areas where it has made a substantial impact is in code review. AI code review is a process that leverages machine learning and other AI technologies to facilitate the evaluation and improvement of software code.
Traditionally, code review is a manual process where fellow programmers examine each other’s code for mistakes, inefficiencies, and improvements. However, this process can be time-consuming and also limited due to the complexity and size of modern codebases. This is where AI code review comes into play. It automates the code review process, enabling faster and more efficient examination of the software code.
AI cannot replace human code review, which is a crucial part of mentoring and collaboration in development teams. But it can provide a basic level of code review for code that otherwise, may not be reviewed at all—in many cases this is a majority of the codebase.
AI code review provides the following key benefits:
- Improves code quality by identifying issues and suggesting improvements
- Detects problems ranging from syntax errors to memory leaks
- Enforces coding standards and best practices
- Identifies security vulnerabilities and suggests remediations
- Provides real-time feedback faster than human reviewers
- Contributes to developer learning and skill improvement
- Offers unbiased reviews, free from human prejudices
How AI code review works
AI code review uses machine learning models to review, analyze, and enhance software code. These algorithms are trained on vast amounts of code data, and knowledge of best coding practices. This learning process enables the AI to identify patterns, detect deviations from good practices, and suggest improvements in the code.
There are two main ways AI code reviews operate:
- Suggestions while coding: AI coding assistants can operate as plugins in an integrated development environment (IDE). Developers get suggestions and modifications to improve the code as they work.
- Review of final code: Developers can actively request a code review of final code they have produced. In this case, an AI code review tool will typically provide a report showing what issues it discovered in the code and how to fix them. This review can take place following a code push or commit, or it may occur in a subsequent phase of the development process.
Code review based on Large Language Models (LLM)
Modern AI assistants leverage LLMs. These are highly complex machine learning models based on the Transformer architecture and trained on huge text datasets. They can understand code and complex instructions and predict the next token or word in code or text. It can generate human-like code and explanations in response to natural language ‘prompts’.
Code review based on LLM provides groundbreaking capabilities, including:
- Context awareness: LLM-based AI code review tools can understand the broader context of the code. Instead of just looking at syntax or isolated functions, they can grasp the overarching intent of a particular module or even the entire application.
- Multi-language support: These tools can seamlessly handle multiple programming languages and their idiomatic nuances. Whether it’s Python’s dynamic typing or Java’s strict object-oriented structure, the AI can navigate and suggest improvements specific to each language’s ecosystem.
- Customization and adaptability: With the power of transfer learning and fine-tuning, LLM-based code review tools can be trained on codebases that have desirable properties, such as high quality or security, or on an organization’s in-house code. This allows them to align more closely with an organization’s requirements, creating a tailored review experience.
- Code-to-comment matching: Ensuring that comments in the codebase align with the functionality is crucial for maintainability. LLMs can detect discrepancies between code and its associated comments, suggesting more accurate documentation when needed.
- Semantic error detection: Beyond syntax, LLMs can detect semantic errors by understanding the intended functionality and pinpointing where the code might not align with that intent.
By combining traditional rule-based analysis with the nuanced understanding of LLMs, AI code review achieves a comprehensive and deep inspection of codebases, providing developers with actionable and precise feedback.
Features and capabilities of AI code review
Automated error detection and fixes
One of the key features of AI code review is its ability to detect errors in the code automatically. It uses advanced algorithms to identify potential bugs, syntax errors, and other issues that could cause problems in the software’s functionality.
An AI code review system also suggests fixes for these issues, providing developers a clear path to rectify the problem. This can significantly reduce the time it takes to debug and refactor code.
Code quality assessment
AI code review can evaluate the overall quality of the code. It uses many factors to determine this, including code readability, maintainability, complexity, and adherence to coding standards.
By assessing the code’s quality, the AI can provide developers with a holistic view of their work. It helps them understand not just what is wrong with their code but also what is right and how they can improve it further.
Code optimization suggestions
Another powerful capability of AI code review is its ability to suggest optimizations to the code. It uses its understanding of good coding practices and its knowledge of the project requirements to identify areas where the code could be made more efficient or effective.
These optimization suggestions could include ways to simplify complex code structures, reduce redundancy, or improve performance. By implementing these suggestions, developers can ensure their code is as optimized as possible, leading to better software performance and user experience.
Compliance and security checks
AI code review can also help ensure the code complies with relevant regulations and is secure against potential threats. It can check for compliance with coding standards and best practices, ensuring the code meets the necessary quality and performance standards.
On the security front, an AI code review system can look for potential vulnerabilities in the code that could be exploited by malicious actors. It provides developers with information on these vulnerabilities and suggests ways to mitigate them, helping to ensure the software is secure.
AI coding assistants based on LLMs can provide a chat interface that allows developers to provide nuanced, natural language instructions for code reviews.
For example, a developer can say: “Check if there is a risk of memory leaks in this code,” and the AI assistant will focus on this specific issue. Developers can also request code reviews of large bodies of code, such as: “Find all uses of log4j in module X and replace it with another library”. Chat interfaces create many new possibilities for automated code review by AI tools.
How code review works in popular AI coding assistants
Tabnine is an AI assistant tool used by over 1 million developers from thousands of companies worldwide. It is designed for enterprise use cases, avoiding exposure to copyleft licenses, and addressing privacy, security, and compliance risks. It can be locally adapted to your codebase and knowledge base without exposing your code. In addition, Tabnine can be deployed on-premises or in a virtual private cloud (VPC) for maximum privacy and security.
Tabnine provides contextual code review that helps developers produce high-quality, industry-standard code. Tabnine’s code suggestions are based on Large Language Models that are exclusively trained on credible open-source licenses with permissive licensing. Tabnine optimizes entire code lines and functions based on individual developers’ unique codes and preferences while keeping the privacy of all users.
GitHub Copilot is an AI coding partner that reviews code and provides suggestions in real time. Developers can initiate GitHub Copilot’s suggestions either by beginning to code, or by typing a natural language comment saying what they want the code to accomplish.
GitHub Copilot assesses the context of files a developer is working on and any related files, and shares its recommendations within the text editor. Its suggestions are powered by a large language model collaboratively operated by GitHub, OpenAI, and Microsoft.
While GitHub Copilot has impressive capabilities, there are several challenges it raises in an enterprise setting. Copilot may sometimes generate insecure, or vulnerable code, because it is trained on large datasets, which may include insecure code. In addition, it might provide code that is copyrighted or licensed under non-permissive licenses, which creates legal exposure for organizations. In addition, since Copilot is a cloud-based service, and cannot be deployed on-premises, businesses should consider potential data privacy and confidentiality concerns.
Amazon CodeWhisperer is an AI coding assistant that provides single-line or full function code recommendations directly in the Integrated Development Environment (IDE). As developers work, CodeWhisperer reviews their code and evaluates English language comments and surrounding code, inferring what code is required to wrap up the task at hand. It can provide multiple suggested code snippets, allowing developers to select the most appropriate one.
CodeWhisperer’s suggestions are powered by large language models (LLMs). These LLMs are trained on billions of lines of code, including Amazon’s proprietary code and open-source code repositories.
CodeWhisperer is a powerful tool, but it has several important limitations for enterprises. Although it does provide some features to verify the security and quality of the code, it could still generate code that does not meet an organization’s quality or security requirements. Additionally, CodeWhisperer requires access to your source code to generate suggestions. Organizations must ensure proper data protection measures and compliance with relevant regulations.
Snyk Code is an AI coding assistant that focuses on security code reviews. It incorporates Static Application Security Testing (SAST) during the coding phase of the development process. This allows devs to construct secure software from the get-go, mitigating the need to discover and rectify issues after the code has been compiled. Snyk Code integrates seamlessly with the IDEs and Source Code Management (SCMs) platforms where devs construct and scrutinize code, delivering swift, actionable, and relevant results to address issues in real time.
You can utilize Snyk Code in your IDE or SCM (via the auto PR checks feature) to recognize issues as they infiltrate your code. Like other AI coding assistants, it evaluates code as developers type, and provides immediate suggestions without requiring compilation.
When Snyk Code spots an issue, it provides practical suggestions for remediation, based on code with similar patterns and data flows in other projects. These case-based examples offer a relevant blueprint for addressing the issue, cutting down on the time required for researching and remediating security issues.
Limitations and challenges of AI code review
AI code review tools, unquestionably beneficial, do exhibit certain imperfections. Presently, this technology grapples with specific constraints, which we anticipate will be mitigated through future enhancements.
While AI code review tools can be extremely helpful, they are not perfect. One common issue is the occurrence of false positives and negatives. These are instances where the tool incorrectly flags an issue or fails to detect a real problem.
False positives can be particularly frustrating for developers, as they can lead to unnecessary work and confusion. They can also undermine confidence in the tool, leading developers to ignore its suggestions or even stop using it altogether.
Limited context understanding
Another limitation of AI code review tools is their limited understanding of context. While these tools are excellent at detecting syntactical issues or simple logical errors, they struggle with more complex issues that require a deep understanding of the code’s context and purpose.
For example, an AI tool might not understand the business logic behind a piece of code, or it might not be aware of the specific requirements of the project. This can result in the tool overlooking significant issues or suggesting incorrect fixes. These limitations are significantly reduced, but not eliminated, by the introduction of LLMs.
Difficulty with complex code
AI code review tools can also struggle with complex code. These tools are typically trained on a large amount of code data, and they perform best when the code they are reviewing is similar to the code they were trained on.
However, when faced with complex or unfamiliar code, these tools can struggle. They might fail to understand the code’s structure or purpose, or they might make incorrect assumptions. This can result in inaccurate reviews and feedback.
Dependence on quality training Data
Finally, the effectiveness of AI code review tools is heavily dependent on the quality of the training data they are provided with. These tools learn from the code data they are trained on, and if this data is flawed or limited, the tool’s performance will suffer.
This highlights the importance of providing these tools with a diverse and comprehensive set of training data. The data should cover a wide range of coding scenarios and should include both good and bad examples of code.
Most importantly, AI code assistants should be trained on code that is known to be of high quality, secure, and in line with the organization’s requirements. Otherwise the code and suggestions it provides could be risky or inappropriate.
Incorporating AI into engineering teams
Enterprises looking to incorporate AI into their engineering teams must prioritize security and privacy compliance. Tabnine, an enterprise-grade AI assistant designed for engineering organizations, places a strong emphasis on safeguarding the privacy and security of your code. It does not store or share user code, whether you opt for SaaS, VPC, or On-Prem deployment, ensuring the confidentiality of your code.
What sets Tabnine apart from other AI coding assistants is its fully isolated models, devoid of any third-party connectivity. This ensures that your code remains secure and free from external access or interference. Furthermore, Tabnine’s generative AI is exclusively trained on open-source code with permissive licenses, assuring compliance with licensing restrictions and ethical considerations. For more information on how Tabnine Enterprise can benefit your organization, contact our enterprise expert.
The landscape of code review is evolving rapidly, with AI-driven tools playing a growing role. AI code review, leveraging the capabilities of Large Language Models and other AI technologies, promises a future where code assessment is quicker, more efficient, and less biased. These tools provide numerous advantages, from error detection and optimization suggestions to nuanced understandings of context and multi-language support.
However, while they undoubtedly enhance the code review process, AI code review tools are not without their limitations. Challenges like false positives, context understanding, and dependence on quality training data remind us that AI tools should complement, not replace, human expertise. As AI code review continues to develop and improve, it will serve as a robust partner to human developers, facilitating the creation of efficient, high-quality, and secure software.